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1 Message
We can now do better than this vague hope!
The founders of Effective Altruism took ideas from Philosophy, Economics, and other parts of academia to build a rigorous approach to ‘doing the most good in the world’, and to exploring and measuring this.
Miraculously, EA also has a passionate and influential group of supporters, and a substantial pool of funds for research, interventions, and advocacy!
EA and global priorities research offers a huge opportunity for academic researchers to have an positive impact (on the allocation of funds, and on the market of ideas).
There are opportunities for funding to support your research within academia, to promote the impact of your research (and gain valuable feedback), to help students find meaningful careers/research
…and/or you may want to leave academia work directly for an EA-aligned organization (like I did).
2 Introduction: Effective Altruism
2.1 ’What is Effective Altruism?
Doing the ‘most good’ given limited resources (some relationship to utilitarianism)… but how do we define ‘the most good’?
What do the most (self reported) ‘highly engaged effective altruists’ prioritize most highly?
2020 EA survey: all responses
(
priority_ordered_bar <- lik_priority_eas20 %>%
as.data.frame() %>%
select(-engagement_num) %>%
likert() %>%
plot(.,
type="bar") +
ggtitle(title)
)Figure 2.1: All responses
… ‘Highly engaged’ (self-rated)
(
priority_ordered_bar_eng <- lik_priority_eas20 %>%
filter(engagement_num==5) %>%
select(-engagement_num) %>%
as.data.frame() %>%
likert() %>%
plot(type="bar") +
ggtitle(title)
)Figure 2.2: Highly engaged only (rated 5/5)
How much effective altruism/global priorities research funding is there?
Open philanthropy … the largest (but not only) grantmaker
.kable_styling <- hijack(kableExtra::kable_styling, full_width=FALSE)
.kable <- hijack(knitr::kable, format.args = list(big.mark = ",", scientific = FALSE))
(
op_research_grants_tab <-
open_phil_grants %>%
filter(possible_research==TRUE) %>%
group_by(year) %>%
dplyr::summarise(total = format(sum(amount, na.rm = TRUE), big.mark=",", scientific=FALSE), grants = n()) %>%
arrange(-year) %>%
mutate(year=as.character(year)) %>%
.kable(caption = "Open Phil (likely) research funding by year") %>%
.kable_styling()
)| year | total | grants |
|---|---|---|
| 2021 | 18,877,180 | 11 |
| 2020 | 77,681,029 | 76 |
| 2019 | 122,463,958 | 63 |
| 2018 | 38,589,084 | 52 |
| 2017 | 57,666,403 | 45 |
| 2016 | 22,386,936 | 27 |
| 2015 | 2,591,000 | 9 |
| 2014 | 1,437,720 | 3 |
| 2013 | 445,000 | 2 |
By year and focus area:
(
op_res_grants_tab_yr_area <-
open_phil_grants %>%
filter(possible_research==TRUE) %>%
dplyr::group_by(year, focus_area) %>% # drop_na(!!yvar, !!treatvar) %>%
summarise(total = sum(amount_usd_k, na.rm = TRUE)) %>%
spread(year, total, fill=0) %>%
arrange(-`2020`) %>%
.kable(caption = "OpenPhil (likely research) grants by year and area, in $1000 USD") %>%
.kable_styling()
)## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
| focus_area | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
|---|---|---|---|---|---|---|---|---|---|
| Scient. Res. | 0 | 0 | 0 | 7,085 | 27,500 | 19,720 | 43,770 | 43,751 | 672 |
| Biosec. | 0 | 0 | 300 | 1,943 | 7,747 | 8,704 | 1,625 | 12,792 | 1,000 |
| AI risk | 0 | 0 | 1,186 | 6,333 | 10,798 | 3,128 | 61,288 | 10,891 | 15,185 |
| Glob. Catastr. | 0 | 0 | 0 | 3,070 | 3,758 | 0 | 1,703 | 4,586 | 1,500 |
| Farm Animal | 0 | 0 | 0 | 820 | 2,022 | 4,580 | 3,296 | 3,261 | 519 |
| Other | 0 | 0 | 10 | 500 | 1,550 | 47 | 1,050 | 806 | 0 |
| Glob. Health/Dev. | 0 | 0 | 0 | 0 | 2,864 | 210 | 3,176 | 678 | 0 |
| Macro-econ | 0 | 0 | 0 | 700 | 0 | 700 | 1,150 | 600 | 0 |
| Immig. Pol. | 0 | 1,185 | 390 | 0 | 0 | 400 | 0 | 200 | 0 |
| Crime/Justice | 445 | 0 | 180 | 1,636 | 1,427 | 1,101 | 5,066 | 115 | 0 |
| Land Ref. | 0 | 0 | 275 | 300 | 0 | 0 | 340 | 0 | 0 |
| US pol. | 0 | 253 | 250 | 0 | 0 | 0 | 0 | 0 | 0 |
(
op_res_grants_line <-
open_phil_grants %>%
group_by(year, focus_area) %>%
mutate(total = sum(amount_usd_k, na.rm = TRUE)) %>%
ggplot() +
aes(x = year, y = amount_usd_k, colour = focus_area) +
geom_jitter(width = 0.5, height = 0.2, size=0.8) +
scale_colour_discrete(labels = function(x) str_wrap(x, width = 15)) +
geom_line(aes(x=year, y=total)) +
ylab("Grant amounts in $1k")
)(
open_phil_grants %>%
filter(possible_research==TRUE) %>%
filter(year==2020) %>%
group_by(Focus.Area) %>%
summarise(total = format(sum(amount, na.rm = TRUE), big.mark=",", scientific=FALSE), grants = n()) %>%
dplyr::arrange(-grants) %>%
.kable(caption = "Open Phil (likely) research funding, 2020") %>%
.kable_styling()
)| Focus.Area | total | grants |
|---|---|---|
| Scientific Research | 43,750,718 | 33 |
| Farm Animal Welfare | 3,261,351 | 13 |
| Potential Risks from Advanced Artificial Intelligence | 10,891,345 | 8 |
| Biosecurity and Pandemic Preparedness | 12,792,330 | 7 |
| Global Health & Development | 678,358 | 5 |
| Criminal Justice Reform | 115,000 | 3 |
| Global Catastrophic Risks | 4,586,224 | 2 |
| Macroeconomic Stabilization Policy | 600,000 | 2 |
| Other areas | 805,703 | 2 |
| Immigration Policy | 200,000 | 1 |
By organization
(
op_res_grants_tab_orgs_area <-
open_phil_grants %>%
filter(possible_research==TRUE) %>%
dplyr::group_by(Organization.Name) %>% # drop_na(!!yvar, !!treatvar) %>%
summarise(total = sum(amount_usd_k, na.rm = TRUE), `number of grants` = n()) %>%
arrange(-total) %>%
filter(total>5000) %>%
.kable(caption = "OpenPhil (likely research) grants by corganization and area, in $1000 USD") %>%
.kable_styling()
)| Organization.Name | total | number of grants |
|---|---|---|
| Georgetown University | 55,250 | 2 |
| UC Berkeley | 29,552 | 18 |
| Nuclear Threat Initiative | 20,439 | 6 |
| Sherlock Biosciences | 17,500 | 1 |
| Machine Intelligence Research Institute | 14,756 | 5 |
| University of Washington (Institute for Protein Design) | 11,368 | 1 |
| Stanford University | 8,294 | 1 |
| Open Phil AI Fellowship | 6,760 | 4 |
| Arizona State University | 6,421 | 1 |
| University of Southern California | 6,238 | 3 |
| Rutgers University | 5,982 | 2 |
| MIT Synthetic Neurobiology Group | 5,970 | 2 |
| Stanford University | 5,752 | 10 |
| Telethon Kids Institute | 5,300 | 1 |
| Foundation for Food and Agriculture Research | 5,292 | 6 |
| Harvard University | 5,068 | 3 |
3 Other organizations funding or doing EA/GP research, or likely interested
Maybe 500 million USD per year in EA/adjacent donations (+ about 250 million from OpenPhil)
GiveWell moving ~$80M per year
Founders Pledge
Longview
Effective Giving
EA Funds
“Gates Foundation seems to do ~$100M-500M/yr of grants in global economic development that seem to have cost-effectiveness on par with GiveWell work”
It’s not all research funding, but some of it is, and it is all interested in prioritization/effectiveness research.
4
knitr::include_url("https://eaforum.issarice.com/posts/f6kg8T2Lp6rDqxWwG/list-of-ea-related-organisations", height = "800px")Research orgs (partial list):
- Future of Humanity Institute (about 70 including affiliates, fellows, affiliates)
- Global Priorities Institute (about 20 researchers/affiliates)
- Rethink Priorities (11 researchers, hiring more)
- GiveWell (about 10 researchers/advisors)
- Animal Charity Evaluators (5-ish)
- Machine Intelligence Research Institute (about 20)
How do most ‘effective altruists’ engage; what is most strongly advocated?
2019 survey: career plans
EAs are moving towards pursuing impact through their careers.
Since 2015 “80,000 Hours thinks that only a small proportion of people should earn to give long term” (MacAskill)
There is also some support for politial influence, at least they say “the hour you spend voting is likely to be the most impactful one in your entire year on average… …influence over how hundreds of thousands or millions of dollars are spent.”
Careers: high vs less engaged
5 My story
Reinstein CV
Telling you my story because it might help you understand the strengths and limitations of academia and working at an EA org, and whether this aligns with your interests.
From my web CV …
Berkeley:
- Proposing ‘new ideas’ like the Poor Card
Limited audience: ‘How does this inform government policy?’, ‘How does it inform/relate to standard Economics (tractable mathematical) models of optimization?’, ‘Will this publish well’?
Considering ideas with a pre-EA policy audience.
‘Does one donation come at the expense of another’?
Should an ‘efficient altruist’ purchase ‘fair trade’ products, bundling consumer choices with additional revenue to poor farmers/workers?
Things I care about: lining up with concepts in the discipline? But I really cared about ideas and impact.
Essex, UK:
Experiments/trials and observational work on charitable and gift-giving: social influences, types of income/uncertainty
Applied microeconomic theory
Moved to Exeter, UK
to pursue ‘Impact’ (ESRC grant, REF focus)
Building teaching/research/outreach resources, such as
- innovationsinfundraising.org and ‘barriers to effective giving’
- “Researching and writing for Economics students”
Why academia/Exeter?
Positives: A fairly supportive environment, research freedom, many great colleagues, moderate teaching, targets ‘deep and rigorous theoretical work’, some of the smartest people
Limitations: academic politics and poor upper-management, countervailing rewards system, constant discussion of points/games (value drift), students
Standard publications as the only way to prove value; limits collaborative and nonstandard work
UK academia rewards either ‘REF-points publications’, box-ticking accreditations, or currying favor with ‘admin’